Human-object interaction (HOIs) detection is a new branch of visual relationship detection, which plays an important role in the field of image understanding. Because of the complexity and diversity of image content, the detection of HOIs is still an onerous challenge. Unlike most of the current works for HOIs detection which only rely on the pairwise information of a human and an object, we propose a graph-based HOIs detection method that models context and global structure information. Firstly, to better utilize the relations between humans and objects, the detected humans and objects are regarded as nodes to construct a fully connected undirected graph, and the graph is pruned to obtain an HOI graph that only preserving the edges connecting human and object nodes. Then, in order to obtain more robust features of human and object nodes, two different attention-based feature extraction networks are proposed, which model global and local contexts respectively. Finally, the graph attention network is introduced to pass messages between different nodes in the HOI graph iteratively, and detect the potential HOIs. Experiments on V-COCO and HICO-DET datasets verify the effectiveness of the proposed method, and show that it is superior to many existing methods.

人与物体交互（HOIs）检测是视觉关系检测的一个新分支，在图像理解领域起着重要的作用。由于图像内容的复杂性和多样性，HOI的检测仍然是一个艰巨的挑战。与当前大多数仅依靠人和物体的成对信息进行HOI检测的工作不同，我们提出了一种基于图的HOIs检测方法，该方法对上下文和全局结构信息进行建模。首先，为了更好地利用人与物之间的关系，将检测到的人与物作为节点，构造完全连接的无向图，并对图进行修剪，得到仅保留连接人与物节点的边缘的HOI图。然后，为了获得人和对象节点的更强大的功能，提出了两种不同的基于注意力的特征提取网络，分别对全局和局部上下文进行建模。最后，引入图注意力网络来迭代HOI图中不同节点之间的消息传递，并检测潜在的HOI。在V-COCO和HICO-DET数据集上进行的实验验证了该方法的有效性，并表明它优于许多现有方法。